title: A comparison of multi-label techniques based on problem transformation for protein functional prediction.
abstract: A comparative analysis of four multi-label classification methods is performed in order to determine the best topology for the problem of protein function prediction, using support vector machines as base classifiers. Comparisons are done in terms of performance and computational cost of parallelized versions of the algorithms, for determining its applicability in high-throughput scenarios. Results show that the performance of the binary relevance strategy, together with a technique of class balance, remains above several recently proposed techniques for the problem at hand, while employing the smallest computational cost when parallelized. However, stacked classfiers and chain classifications can be conveniently used in pipelines, due to the low number of false positives reported.
authors.forename: A F
authors.initials: AF
authors.lastname: Giraldo-Forero
authors.forename: J A
authors.initials: JA
authors.lastname: Jaramillo-Garzón
authors.forename: C G
authors.initials: CG
authors.lastname: Castellanos-Domínguez
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D019669:Embryophyta / Q000378:metabolism; D011506:Proteins / Q000145:classification / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D011506:Proteins